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研究生： 李兆寅
Li, Jhao-yin
論文名稱： 結合隨機隱藏式馬可夫模型之二因子模糊時間序列預測模式
A Study of Forecasting Two-factor Fuzzy Time Series using a Stochastic Hidden Markov Model
指導教授： 李昇暾
Li, Sheng-tun
學位類別： 碩士
Master
系所名稱： 管理學院 - 資訊管理研究所
Institute of Information Management
論文出版年： 2008
畢業學年度： 96
語文別： 英文
論文頁數： 49
中文關鍵詞： 模糊時間序列 、隱藏式馬可夫模式 、模糊關係 、模糊集合 、預測 、蒙地卡羅
外文關鍵詞： hidden Markov model, forecasting, fuzzy time series, Monte Carlo, fuzzy relations, fuzzy sets
相關次數： 點閱：103  下載：3 
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    • 在我們的日常生活中，模糊以及不完全的資料充斥在我們的身邊。因此，近年來，在不確定的環境下模糊時間序列預測漸漸地扮演著越來越重要的角色。本研究擬提出以隱藏式馬可夫模式為基礎之新的模糊時間序列預測模式。希望透過隱藏式馬可夫模式此一有系統的機率模式，利用狀態間的轉移機率來處理模糊時間序列預測問題。並且，本預測模式結合了蒙地卡羅模擬法，希望透過多次的抽樣實驗，將不論機率大小的實驗結果皆納入考量，使得最終預測結果能較貼近實際結果，並獲得較低的預測誤差率。最後，本研究將把所提出的預測模式實際應用於台北地區每日平均溫度與雲量資料，藉此以完整說明本研究所提出之預測流程，並透過均方誤與平均預測誤差率此二衡量指標，說明本研究所提出之預測模式較過去相關的模糊時間序列預測模式，擁有較低的預測誤差率。

    In various areas, vague and incomplete data described as linguistic variables massively exists in our daily life. The goal of fuzzy time series forecasting under uncertain situations becomes more and more important but difficult as well. The transitions of states in a system are probabilistic due to the inherent uncertainty involving time evolution. In this paper, we present a new forecasting model based on Hidden Markov Model for fuzzy time series to understand the probability of the transition of states and acquire the forecasting outcome using simple Monte Carlo’s simulation method in order to achieve the true performance of the model approximately. Based on the model, experiments of temperature prediction could be developed, where the data of daily average temperature and average cloud density from June to September, 1993 to 1996 in Taipei are used to illustrate the forecasting process. The experiments validate the better accuracy of the proposed model achieved over traditional fuzzy time series models by training data.

    Abstract(chinese) I Abstract II Acknowledgement(chinese) III List of Tables IV List of Figures V CHAPTER I Introduction 1 1.1 Background and motivation 1 1.2 The goal and contribution of this thesis 2 1.3 The structure of this thesis 3 1.4 The limitation of this thesis 4 CHAPTER II Literature Review 6 2.1 Fuzzy theory & fuzzy time series 6 2.1.1 Fuzzy set 6 2.1.2 Membership function 7 2.1.3 Fuzzy time series 8 2.2 Fuzzy time series forecasting model 9 2.2.1 Time-variant model 10 2.2.2 Time-invariant model 11 2.3 The concept of hidden Markov model 16 2.4 Summary 17 CHAPTER III Model Development 19 3.1 Framework of the whole model 19 3.2 Hidden Markov model 20 3.2.1 The definition of hidden Markov model 21 3.2.2 Viterbi algorithm 22 3.3 Forecasting model 23 CHAPTER IV Experiment and Evaluation 29 4.1 Experiment on HMM based model 29 4.2 Performance evaluation and comparison 38 4.2.1 Forecasting accuracy 39 4.2.2 Verification and results 39 4.3 Monte Carlo simulation 41 4.4 Discussion 44 CHAPTER V Conclusions and Future Work 45 5.1 Conclusions 45 5.2 Future work 46 References 47

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